Exploring the Dynamics of Data Transmission in 5G Networks: A Conceptual Analysis

• URL: http://arxiv.org/abs/2404.16508v1
• Date: Thu, 25 Apr 2024 11:02:54 GMT
• Title: Exploring the Dynamics of Data Transmission in 5G Networks: A Conceptual Analysis
• Authors: Nikita Smirnov, Sven Tomforde,
• Abstract summary: This conceptual analysis examines the dynamics of data transmission in 5G networks. It addresses various aspects of sending data from cameras and LiDARs installed on a remote-controlled ferry to a land-control center.
• Score: 1.3351610617039973
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This conceptual analysis examines the dynamics of data transmission in 5G networks. It addresses various aspects of sending data from cameras and LiDARs installed on a remote-controlled ferry to a land-based control center. The range of topics includes all stages of video and LiDAR data processing from acquisition and encoding to final decoding, all aspects of their transmission and reception via the WebRTC protocol, and all possible types of network problems such as handovers or congestion that could affect the quality of experience for end-users. A series of experiments were conducted to evaluate the key aspects of the data transmission. These include simulation-based reproducible runs and real-world experiments conducted using open-source solutions we developed: "Gymir5G" - an OMNeT++-based 5G simulation and "GstWebRTCApp" - a GStreamer-based application for adaptive control of media streams over the WebRTC protocol. One of the goals of this study is to formulate the bandwidth and latency requirements for reliable real-time communication and to estimate their approximate values. This goal was achieved through simulation-based experiments involving docking maneuvers in the Bay of Kiel, Germany. The final latency for the entire data processing pipeline was also estimated during the real tests. In addition, a series of simulation-based experiments showed the impact of key WebRTC features and demonstrated the effectiveness of the WebRTC protocol, while the conducted video codec comparison showed that the hardware-accelerated H.264 codec is the best. Finally, the research addresses the topic of adaptive communication, where the traditional congestion avoidance and deep reinforcement learning approaches were analyzed. The comparison in a sandbox scenario shows that the AI-based solution outperforms the WebRTC baseline GCC algorithm in terms of data rates, latency, and packet loss.

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